News
Adobe’s New AI Agents: Revolutionizing Enterprise Customer Experience Management
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On September 10, 2025, Adobe unveiled a suite of AI agents under its new Adobe Experience Platform Agent Orchestrator. This initiative is poised to fundamentally reshape how large enterprises build, deliver, and optimize customer experiences. By introducing agentic AI—systems capable of planning, reasoning, and acting within workflows—Adobe signals a new era where automation and personalization are tightly integrated into everyday business operations, while preserving the human element in oversight and decision-making.
The Big Picture: What Adobe Announced
At the heart of Adobe’s announcement is the Agent Orchestrator, a platform designed to help companies deploy, manage, and customize AI agents. This new layer sits on top of the existing Adobe Experience Platform (AEP), enhancing its capabilities significantly. The orchestrator is composed of several key components.
First, there’s the conversational interface, which enables users to interact with the system via Adobe’s AI Assistant. This user-friendly layer translates prompts into actionable insights. Then, the reasoning engine takes over, interpreting natural language commands, planning tasks—often complex and multi-step—and determining which AI agents to activate. These decisions are informed by a centralized knowledge base, which includes context, brand policies, user roles, and organizational priorities. This ensures that AI decisions are grounded in the company’s actual data and strategic objectives.
One of the most anticipated features is the Agent Composer, expected to launch soon. It will allow businesses to customize AI agents to align more precisely with their unique goals and workflows.
Adobe also noted that over 70 percent of AEP customers are already using the AI Assistant. Major brands like The Hershey Company, Lenovo, Wegmans, Wilson, and Merkle are already leveraging these new agentic features, indicating early momentum and industry interest.
What the AI Agents Can Do
Adobe’s initial offering includes six domain-specific agents, each built to tackle different aspects of customer experience management.
The Audience Agent is designed to help businesses build, scale, and optimize customer segments. It can recommend high-value audience groups and monitor how well these segments perform against marketing KPIs. The Journey Agent focuses on orchestrating customer journeys across digital channels like websites, mobile apps, and email. It optimizes touchpoints, detects drop-offs, and suggests improvements to keep customers engaged.
The Experimentation Agent supports A/B testing and other experimental frameworks by analyzing performance data and predicting conversion rates or lift. It integrates directly with Adobe’s Journey Optimizer through a tool called the Experimentation Accelerator. Meanwhile, the Data Insights Agent offers powerful visualizations and forecasting tools, scanning internal data and signals to identify issues and recommend actions.
For web performance, the Site Optimization Agent keeps digital properties running smoothly by flagging issues like broken links or slow-loading pages. Lastly, the Product Support Agent is built for customer service workflows, helping diagnose and resolve issues using internal knowledge bases and tracking support tickets within existing systems.
Security, Governance & Customization
Adobe emphasized the importance of maintaining strong governance over these new AI systems. To that end, its AI agents come with enterprise-grade controls for data collection, storage, usage, and consent. These mechanisms are designed to ensure compliance with data privacy regulations and internal brand standards.
The agents operate under strict governance frameworks that include human-in-the-loop decision-making and AI ethics oversight. Adobe’s internal AI ethics review board is responsible for the design, training, testing, and deployment of all agents. Additionally, Adobe is preparing to roll out tools like the Agent Composer, Agent SDK, and Agent Registry. These will help companies and developers build, extend, and coordinate agents to suit their specific needs and environments.
Why It Matters & Where It Fits
This announcement comes at a critical time for enterprise technology. Businesses are drowning in customer data—ranging from web interactions to mobile app usage—but many still struggle to transform that data into coherent, personalized customer experiences. Adobe’s approach aims to bridge this gap by combining rich data, sophisticated reasoning, and intelligent action through its AI agents.
There’s also a broader shift in the enterprise AI landscape. Companies are moving from reactive AI systems that simply respond to prompts toward more proactive agentic systems that can plan and act independently. Adobe’s AI agents reflect this trend by providing not just answers, but also strategies and execution paths. And because Adobe’s tools are tightly integrated within its ecosystem, companies already using its Experience Platform could gain a competitive edge. However, the move also raises the stakes for competitors and non-Adobe users to match this level of AI integration.
Challenges & Open Questions
Despite its promise, Adobe’s agentic framework is not without challenges. Customizing agents using tools like the Agent Composer or SDKs could require significant effort, including new testing protocols and changes to internal workflows. Enterprises will need to invest time and expertise to ensure these tools are both effective and safe to use.
Data privacy remains a central concern. The more decisions are automated, especially those involving customer data, the more regulatory and ethical scrutiny companies may face. Ensuring full compliance with laws like GDPR and CCPA, while maintaining transparency with customers, will be essential.
Another key issue is trust. While these agents are capable of acting autonomously, they are not infallible. Organizations must have systems in place to monitor performance, detect errors, and intervene when necessary. A poorly executed recommendation or a mismanaged customer journey could erode trust and damage brand loyalty.
Finally, the issue of interoperability looms large. For companies using software outside of Adobe’s stack, integration could prove complex. There’s also the risk of vendor lock-in, which might limit flexibility in the long term.
The Road Ahead
Adobe has made clear that this is just the beginning. Future developments will emphasize even more customization, ecosystem integration, and partnerships. The upcoming Agent Composer tool is a key part of this vision, enabling companies to fine-tune AI agents for their specific domains. Adobe is also working closely with consulting firms like PwC, Cognizant, and Omnicom, as well as cloud infrastructure providers like Google Cloud, to scale adoption and offer domain-specific configurations.
As AI agents mature, performance metrics—such as speed of deployment, customer conversion rates, and operational efficiency—will likely become the benchmarks for success. Competitors like Salesforce and Microsoft are also investing in agentic AI capabilities, suggesting a highly competitive and rapidly evolving landscape.
Bottom Line
Adobe’s new suite of AI agents, powered by the Experience Platform Agent Orchestrator, represents a significant step forward in enterprise customer experience management. For companies willing to invest in infrastructure, governance, and customization, these agents could offer transformative benefits—faster decision-making, richer personalization, and greater operational efficiency.
However, the road to full adoption will require thoughtful implementation, strong data governance, and continuous human oversight. If Adobe can deliver on its promises and businesses can execute responsibly, this could mark the dawn of a new era in enterprise AI.
AI Model
How to Prompt Nano Banana Pro: A Guide to Creating High-Quality Images with Google’s AI
Why Nano Banana Pro Matters
Nano Banana Pro is Google DeepMind’s most advanced image generation model, built on the powerful Gemini 3 Pro architecture. It delivers high-resolution outputs (up to 4K), understands complex prompts with layered context, and performs exceptionally well when generating realistic lighting, textures, and dynamic scenes. It also supports image referencing — letting you upload photos or designs to guide the visual consistency.
In short, it’s not just a toy — it’s a tool for designers, marketers, illustrators, and creatives who want to build professional-grade images fast. But to unlock its full potential, you need to learn how to prompt it properly.
Prompting Basics: Clarity Beats Cleverness
The secret to powerful results isn’t trickery — it’s clarity. Nano Banana Pro doesn’t need keyword spam or obscure syntax. It needs you to be specific and structured.
Here are the key rules to follow:
- Be descriptive, not vague: Instead of “a cat,” write something like “a ginger British shorthair cat sitting on a marble countertop under soft morning light.
- Layer your descriptions: Include details about the subject, setting, atmosphere, materials, lighting, style, and mood.
- State your format: Tell the model if you want a photo, digital painting, cinematic frame, 3D render, infographic, comic panel, etc.
- Use reference images: Nano Banana Pro supports multiple uploads — useful for matching styles, poses, faces, characters, or branding.
This is how professionals prompt: not by hacking the system, but by being precise about what they want.
Crafting Prompts by Use Case
📸 Realistic Photography
Want a product photo, fashion portrait, or cinematic still? Then your prompt should include lens type, lighting style, subject age, composition, and color grading.
Example:
Professional studio portrait of a 35-year-old woman in natural light, soft cinematic lighting, shallow depth of field, 85mm lens look, natural skin tones, soft shadows, clean background, editorial style.
Another example:
A 3/4 view of a red sports car parked in a luxury driveway at golden hour, realistic reflections, soft shadows, DSLR-style image, bokeh background.
These prompt structures help the model replicate not just the subject but the feel of a professionally shot image.
🎨 Illustration, Comic Art, and 3D Concepts
If you want stylized work — like a retro comic, anime-style character, or matte painting — the style must be part of the prompt.
Example:
Comic-style wide cinematic illustration, bold black outlines, flat vibrant colors, halftone dot shading, a heroic female astronaut on Mars with a pink sky, dramatic lighting, wide aspect ratio.
More styles to try:
- Fantasy concept art, a medieval knight riding a dragon above stormy mountains, painted in the style of Frank Frazetta, high detail, dramatic lighting.
- Cyberpunk anime character in a rain-soaked Tokyo alley, glowing neon lights, futuristic fashion, overhead perspective, digital painting.
Tip: Reference known artistic styles (e.g., Art Nouveau, Impressionism, Pixar, Studio Ghibli) to guide the tone.
🔄 Editing Existing Images
Nano Banana Pro can also transform existing images by changing backgrounds, lighting, or adding/removing objects.
Examples:
Replace the background with a rainy city street at night, reflect soft blue and orange lights on the subject, keep original pose and composition, cinematic tone.
Add a glowing book in the subject’s hands, soft magical light cast on their face, night-time indoor setting.
Best practices:
- Use clear “before/after” language.
- Indicate what must stay unchanged.
- Specify the mood or lighting effect you want added.
Common Mistakes to Avoid
- Too generic: A prompt like “a girl standing” tells the model almost nothing. Who is she? Where is she? What’s the style?
- Keyword stuffing: Don’t use outdated tricks like “masterpiece, ultra-detailed, trending on ArtStation.” They’re mostly ignored.
- Ignoring context: Don’t forget to describe how elements relate (e.g. “holding a glowing orb” vs. “glowing orb floating behind her”).
- Unclear intent for text/logos: If you want branded material, say exactly what the logo or label should look like, and where.
Prompt Templates You Can Use Right Now
Try adapting these for your needs:
- “Cinematic 4K photo of a mountain climber reaching the summit at sunrise, orange glow on snowy peaks, lens flare, dramatic sky.”
- “Retro-futuristic 3D render of a diner on Mars, neon signs, dusty surface, stars in the background, warm ambient light.”
- “Isometric vector-style infographic showing renewable energy sources, solar, wind, hydro, with icons and labels.”
- “Realistic photo of a smartwatch product on a floating glass platform, minimalistic white background, soft shadows.”
These prompts are short but rich in visual instruction — and that’s the key to strong output.
Going Further: Advanced Prompting Tips
- Use cinematic language: Words like “soft light,” “overhead shot,” “close-up,” “medium angle,” “shallow depth of field” guide the AI like a film director.
- Test with reference images: Upload an image of your brand, product, or character to maintain continuity.
- Iterate: If your first image isn’t right, adjust one or two variables (e.g., lighting, background, subject age) and regenerate.
- Define aspect ratios: Use “cinematic,” “vertical portrait,” “square crop” if you need a specific format.
- Stay natural: Write prompts like you’re briefing a professional illustrator or photographer.
Final Thoughts
Nano Banana Pro is one of the most powerful visual AI tools available — but it’s only as good as your prompts. Whether you’re an art director, a solo founder, or a content creator, learning to prompt well is the fastest way to unlock its full creative range.
Focus on clarity, visual language, and style specificity. Add references when needed. Think like a photographer, art director, or storyteller. The better your brief, the better the image.
Want more? Ask for our expanded prompt pack: 50+ ready-made formulas across categories like product design, sci-fi art, fantasy scenes, infographics, editorial portraits, and more.
AI Model
Qwen vs. ChatGPT — Which AI Assistant is Better — and For What
Why This Comparison Matters Now
Qwen, the large language model developed by Alibaba Cloud, has recently been gaining significant attention. The release of Qwen 2.5-Max and its successors has sparked comparisons across benchmarks covering reasoning, coding, long-context handling, and multimodal tasks. Meanwhile, ChatGPT continues to dominate as the default choice for many users who prioritize conversational quality, creative tasks, and ease of use. Comparing the two is increasingly important for anyone deciding where to invest their time, money, or infrastructure in 2025.
Let’s explore how Qwen and ChatGPT compare across major performance categories — and which model might suit your needs better.
Where Qwen Shines: Power, Context, and Flexibility
One of Qwen’s strongest features is its ability to handle long-context reasoning and document-heavy workflows. With larger context windows than many competitors, Qwen is particularly adept at analyzing long reports, writing consistent long-form content, summarizing legal or technical material, and managing multi-layered input without losing coherence. It’s a powerful tool for users who need depth.
Qwen also excels in structured logic and code-related tasks. In independent evaluations, it has shown impressive results in mathematical reasoning, data extraction, and code generation. For developers and technical users looking for an AI assistant to support real engineering workflows — rather than simply explain code snippets — Qwen is a highly capable alternative to established incumbents.
Multimodal and multilingual flexibility is another area where Qwen stands out. It supports text, image input, and multiple languages, enabling it to serve as a true assistant across varied communication and media formats. That’s particularly useful for global users or teams operating in bilingual or multilingual environments.
Finally, the open-source accessibility of Qwen is a major advantage. While not every version is fully open, many variants are freely available and can be run locally or fine-tuned. For users prioritizing data control, customization, or cost-efficiency, that’s a serious point in Qwen’s favor.
Where ChatGPT Excels: Conversation, Creativity, and Ecosystem
ChatGPT continues to lead when it comes to polish and user experience. Its conversational flow is smooth, stylistically natural, and often feels more human than any other model on the market. That’s invaluable for creative writing, ideation, storytelling, or any application that requires tone, style, and nuance. It’s also why many casual users prefer ChatGPT over more technical models.
ChatGPT’s integration with live data, APIs, and tools (depending on the version) provides a dynamic and extensible platform for users who need real-time insights or app-level functionality. If you’re looking for an assistant that can browse the web, generate code, search documentation, or plug into third-party services, ChatGPT is often the more mature choice.
Consistency, reliability, and safety mechanisms also remain a strength. For teams or individuals who don’t want to think about model drift, hallucination tuning, or backend parameters, ChatGPT offers a plug-and-play solution that’s hard to beat. It’s a tool that just works — and that simplicity matters more than benchmark scores for a wide audience.
The scale and maturity of ChatGPT’s ecosystem also give it a clear edge. From community guides to business integrations, apps, and workflows — it’s supported nearly everywhere, and that makes it easy to adopt regardless of your skill level.
Limitations and Trade-offs
That said, Qwen and ChatGPT each come with their own trade-offs.
Qwen, while powerful, sometimes lacks the fluency or stylistic finesse that makes ChatGPT feel so natural. It can hallucinate in edge cases, and while some versions are open-source, the most powerful iterations may still depend on Alibaba’s infrastructure, limiting portability for privacy-centric users.
ChatGPT, for its part, is a closed model, with cost barriers and fewer customization options. It also has a more constrained context window in some versions, making it less ideal for ultra-long documents or advanced reasoning across large data structures.
Which Model Should You Use?
If your work involves processing long documents, building tools, working with code, or requiring multilingual support — and you value the ability to run models locally or integrate them deeply — Qwen is an excellent fit. Its performance is strong, and it offers more technical freedom for advanced users.
If your needs are creative, conversational, or content-driven — and you want something intuitive, responsive, and polished out of the box — ChatGPT is still the best experience available today. It’s perfect for brainstorming, writing, email generation, and any task where clarity, creativity, and tone matter.
For enterprise teams, researchers, and power users — using both might be the optimal solution. Qwen can handle the heavy lifting in development and data, while ChatGPT takes care of interaction, presentation, and ideation.
Final Verdict
There’s no absolute winner in the Qwen vs. ChatGPT debate — only better fits for different tasks. Qwen brings muscle, flexibility, and context awareness. ChatGPT delivers fluency, elegance, and seamless usability.
In the AI race of 2025, the smartest move isn’t to pick a side — it’s to pick the right tool for the job.
News
Alibaba’s AI Coup: Qwen App Hits 10 Million Downloads in One Week — And the AI Wars Just Escalated
A Meteoric Debut for Qwen
Alibaba’s freshly launched Qwen app has crossed 10 million downloads in just its first seven days — a staggering adoption rate that places it among the fastest-growing AI applications globally. The explosive start signals more than consumer interest. It marks Alibaba’s transition from infrastructure giant to serious AI contender in the public arena.
Qwen Isn’t Just Another Chatbot
At the core of Qwen’s early success is its engine: the Qwen model family, developed in-house by Alibaba. These large language models (LLMs) are multimodal — capable of processing not just text, but also images, audio, and potentially video. Unlike other AI tools that remain sandboxed in niche applications, Qwen is designed as a true all-in-one assistant.
From drafting documents and summarizing reports to answering questions and managing multimedia tasks, Qwen is built to be useful — not just entertaining. And critically, it launched with a free-access model, eliminating the subscription paywall that often hinders adoption in early-stage AI apps.
From E-Commerce to Everyday AI
This launch represents a clear strategic pivot for Alibaba. Historically known for e-commerce dominance and its powerful cloud infrastructure (via Alibaba Cloud), the company is now positioning itself as a top-tier player in the AI space — not just at the backend, but at the consumer-facing layer.
Qwen is not just a product — it’s a platform play. It ties into Alibaba’s cloud resources, shopping ecosystem, productivity tools, and eventually, financial services. By releasing it as a standalone, viral consumer app, Alibaba is laying the groundwork for a much bigger AI ecosystem play.
Global AI Ambitions, Starting in Asia
While Qwen’s initial rollout is concentrated in China and Southeast Asia, there are clear signs Alibaba intends to push the app globally. With Western alternatives like ChatGPT, Claude, and Gemini facing geopolitical and regulatory barriers in some regions, Qwen could capitalize on being both regionally accessible and locally optimized.
Additionally, the app’s early traction reflects strong demand for AI solutions tailored to regional languages, customs, and ecosystems. As Chinese tech continues to look outward, Qwen may become a cultural as well as a technological export — one capable of competing head-to-head with the biggest names in global AI.
The Next Phase: Monetization and Market Power
Crossing 10 million downloads in a week is only the first milestone. The real test lies in retention, monetization, and integration. Alibaba will now focus on converting casual users into power users, offering advanced features, integrating payments, cloud-based services, and potentially leveraging the app to strengthen its broader commercial footprint.
There is already speculation that Qwen could evolve into the “WeChat of AI” — a super-assistant that combines messaging, productivity, shopping, and finance in a single intelligent interface. If that vision materializes, Alibaba may have just positioned itself as the most powerful AI consumer company outside the West.
Final Thought
The Qwen launch is not just about downloads. It’s about direction. Alibaba has made its move — not with hype or vague roadmaps, but with a working, useful, and widely adopted AI assistant. The global AI race is officially more competitive than ever.
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